» Articles » PMID: 31026282

Comparative Effectiveness Analysis of Medicare Dialysis Facility Survey Processes

Overview
Journal PLoS One
Date 2019 Apr 27
PMID 31026282
Authors
Affiliations
Soon will be listed here.
Abstract

Background: To assure and improve the quality and safety of care provided by dialysis facilities, federal oversight has been conducted through periodic survey assessment. However, with the growing number of individuals living with ESRD and dialysis facilities, state survey agencies have faced challenges in time and resources to complete survey activities. Therefore, the survey process ('Basic Survey' used prior to 2013) was redesigned in order to develop a more efficient process ('Core Survey' newly implemented since 2013). The purpose of this analysis was to evaluate and compare dialysis facility survey outcomes between the Core and Basic Survey processes, using a causal inference technique. The survey outcomes included condition-level citations, total citations (condition- and standard-level), and citation rate per survey-hour.

Methods: For comparisons of non-randomly assigned survey types, propensity score matching was used. Data were drawn from CMS' Quality Improvement Evaluation System (QIES) database from January 1, 2013 through July 31, 2014. Covariates available included survey type, facility characteristics (state, urban, practices catheter reuse, dialysis modalities offered, number of patients, mortality, hospitalization, infection) and survey-related characteristics (number of surveyors, time since last survey).

Results: Compared to the Basic Survey, the Core Survey identified 10% more total citations (P = 0.001) and identified condition-level citations more frequently, although the latter finding did not reach statistical significance. These findings suggest an increase of 10% in citation rate (i.e. ratio between citations and survey time) with the Core survey process (P = 0.002).

Conclusions: Greater efficiency has implications for attenuating the time-intensive burden of the state survey process, and improving the safety and quality of care provided by dialysis facilities.

References
1.
DeSantis S, Lazaridis C, Ji S, Spinale F . Analyzing Propensity Matched Zero-Inflated Count Outcomes in Observational Studies. J Appl Stat. 2013; 41(1). PMC: 3843491. DOI: 10.1080/02664763.2013.834296. View

2.
Gayat E, Thabut G, Christie J, Mebazaa A, Mary J, Porcher R . Within-center matching performed better when using propensity score matching to analyze multicenter survival data: empirical and Monte Carlo studies. J Clin Epidemiol. 2013; 66(9):1029-37. DOI: 10.1016/j.jclinepi.2013.03.018. View

3.
Normand S, Landrum M, Guadagnoli E, Ayanian J, Ryan T, Cleary P . Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores. J Clin Epidemiol. 2001; 54(4):387-98. DOI: 10.1016/s0895-4356(00)00321-8. View

4.
Hall D . Zero-inflated Poisson and binomial regression with random effects: a case study. Biometrics. 2000; 56(4):1030-9. DOI: 10.1111/j.0006-341x.2000.01030.x. View

5.
Walshe K, Harrington C . Regulation of nursing facilities in the United States: an analysis of resources and performance of state survey agencies. Gerontologist. 2002; 42(4):475-87. DOI: 10.1093/geront/42.4.475. View